Rescue vessels are the main actors in maritime safety and rescue operations. At the same
time, aerial drones bring a significant advantage into this scenario. This paper presents the …

Hardware accelerators for convolution neural networks (CNNs) enable real-time
applications of artificial intelligence technology. However, most of the existing designs suffer …

In this paper, an on-device classification of electrocardiography (ECG) with Compressed
Learning (CL) for health Internet of Things (IoT) is proposed. A CL algorithm combining with …

It remains challenging for a Convolutional Neural Network (CNN) accelerator to maintain
high hardware utilization and low processing latency with restricted on-chip memory. This …

Modern Artificial Intelligence (AI) systems deploy Convolution Neural Networks (CNN) as
they offer very high accuracy. Computational complexity of CNNs necessitates hardware …

The latest convolutional neural network (CNN) models for object detection include complex
layered connections to process inference data. Each layer utilizes different types of kernel …

Due to the intensive computational complexity and various types of convolution, it is a
challange to implement different CNN models on a specific hardware. Many previous works …

This brief presents a memory-efficient CNN accelerator design for resource-constrained
devices in Internet of Things (IoT) and autonomous systems. A segmented logarithmic …

Convolutional neural networks (CNNs) have achieved great successes on many computer
vision tasks, such as image recognition, video processing, and target detection. In recent …

This paper presents the architecture of a Convolution Neural Network (CNN) accelerator
based on a new processing element (PE) array called a diagonal cyclic array (DCA). As …